This paper examines a reconfigurable intelligent surface (RIS)-assisted single-input multiple-output (SIMO) frequency-modulated continuous-wave (FMCW) radar system for detecting vital signs, particularly breath rates, of multiple targets. The proposed method employs manifold optimization (MO) to design RIS phase shifts, aiming to maximize the signal-to-interference-plus-noise ratio (SINR) of the received radar signal. The estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm is then applied to estimate the targets' vital signs. Simulation results show that the proposed scheme outperforms both the no-RIS scenario with an added singular value decomposition (SVD)-based processing at the radar receiver and a semidefinite relaxation-based RIS design approach, all while maintaining lower computational complexity.

The simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) enhances millimeterwave (mmWave) communication by enabling full-space coverage, making it ideal for vehicle-to-everything (V2X) applications. This paper explores a multiuser multiple-input single-output (MU-MISO) mmWave non-orthogonal multiple access (NOMA) downlink vehicular environment aided by STAR-RIS, aiming to maximize the sum rate of the infrastructure-to-vehicle (I2V) links through the design of base station (BS) beamforming, power allocation, and STAR-RIS phase shifts under coupled phase constraints. We propose an unsupervised learning model for STAR-RIS phase design, complemented by analytical approaches for BS beamforming and power allocation. Simulations using the simulation of urban mobility (SUMO) software confirm the superior performance of the proposed scheme in various scenarios while meeting STAR-RIS and NOMA requirements.

Robustness of reconfigurable intelligent surface (RIS) has been a concern due to potential hardware impairments (HWI) and imperfect channel state information (CSI) measurements caused by the numerous passive elements on board. Recent studies observe that the impairments not only introduce mis-alignment in phase adjustments but also affect the amplitude of reflected signals, which further complicates the issue. To address this issue, we introduce a novel deep reinforcement learning (DRL)-based discrete optimization framework aimed at mitigating various HWI and CSI imperfections in RIS-assisted millimeter-wave (mmWave) multi-input-single-output (MU-MISO) systems. Employing proximal policy optimization (PPO), our method discretely addresses HWI and CSI challenges without continuous relaxation. Simulation results demonstrate the superiority of our approach over the traditional optimal beamforming baseline in minimizing the worst-case mean squared error (MSE) of the signal received by the users. The code has been made open-source on GitHub, serving as a valuable reference for further research and application in RIS-assisted communication systems.

Precise vehicle positioning is the key foundation for advancing vehicle automation technology beyond level three. However, the conventional global positioning system (GPS) is susceptible to inaccuracies caused by environmental interference. Existing works for improving positioning accuracy either require fundamental infrastructure modification to replace GPS or utilize prior knowledge of environmental information to reduce interfer-ence, where both are impractical in the real world. To improve the GPS-based vehicle positioning system to provide more accurate coordinate estimates without prior knowledge of environmental information, we propose a self-supervised learning architecture composed of four learning methods: hierarchical density-based spatial clustering (HDBSCAN), graph convolution network (GCN), domain-adversarial neural network (DANN), and long short-term memory (LSTM). The proposed framework utilizes both spatial and temporal information in vehicle positioning. The simulation results within our proposed comprehensive framework demonstrate a significant improvement in the accuracy of vehicle coordinate estimates, with the estimation error mean decreasing by 46 % and the error standard deviation decreasing by 34 % compared to the baseline.

Vehicle-to-everything (V2X) communication is an essential technology for future vehicular applications. It is challenging to simultaneously achieve vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications, given the shared spectrum. Deep reinforcement learning (DRL)-based algorithms have been proposed for resource allocation in V2I and V2V designs. Existing DRL designs focus on the objectives of high-capacity V2I and high-reliability V2V links. In this study, a multi-agent DRL algorithm is proposed to maximize the sum capacity of V2I links while ensuring capacity fairness among the V2V links. The simulation results demonstrate the balance between the V2I–V2V objectives achieved by the proposed algorithm.

Frequency-modulation continuous wave (FMCW) radar has been employed to implement a non-contact vital sign monitoring system for future healthcare applications. This paper proposes a multi-target vital sign (heart rate and breath rate) detection scheme with limited channel information for single-input multiple-output (SIMO)-FMCW radar systems in multipath propagation environments. In the proposed method, multipath effect mitigation is first achieved by the decomposition of vital sign signals from self- and mutual-multipath interferences using the multichannel singular spectrum analysis (MSSA) algorithm. Then, the desired vital signs are estimated via the estimation of signal parameters via rotational invariance technique (ESPRIT). Simulation shows that the proposed scheme achieves superior performance in terms of the estimation error in multipath propagation environments.

In recent years, spectrum congestion has become a significant issue. Thus, significant attention has been paid to spectrum-sharing. The dual-function radar-communication (DFRC) system is an attractive solution for the spectrum-sharing problem. Existing studies primarily focus on transmitted beamforming at the base station (BS). In this study, we designed both the transmitted and received beamformers of a DFRC BS to perform multiple-input multiple-output (MIMO) radar sensing and multi-user multiple-input single-output (MU-MISO) communication employing hybrid beamforming. To address the difficulty of the primal problem, we recast the nonconvex design problem into a convex form and subsequently derive suboptimal solutions. Simulation results demonstrate satisfactory performance in terms of the sum-rate, interference mitigation, desired signal enhancement, and complexity of the proposed scheme.

Designing a signal-processing algorithm for frequency-modulated continuous-wave (FMCW) radar applications with advanced functionality remains a challenging problem. Specifically, traditional algorithms improve detection resolution by increasing bandwidth and contribute to inefficient spectral use and maximum range reduction. To strike a balance between the maximum detection range and range resolution, we propose a resolution improvement algorithm based on super-resolution techniques. The low-resolution detection results were used to infer the high-resolution data by emphasizing the hidden spatial correlations. Simulation results confirmed that the proposed algorithm possesses an outstanding ability to achieve high-resolution detection while employing reduced bandwidth, leading to two advantages: spectral efficiency and maximum detectable range.

Vehicle positioning is a key component of autonomous driving. The global positioning system (GPS) is the most commonly used vehicle positioning system currently. However, its accuracy will be affected by environmental differences and thus fails to meet the requirements of meter-level accuracy. We consider a coordinate neighboring vehicle positioning sys-tem (CNVPS) based on GPS, omnidirectional radar, and V2V communication ability to obtain additional information from neighboring vehicles to improve the GPS positioning accuracy of vehicles in various environments. We further use the concept of transfer learning (TL) wherein an adversarial mechanism is designed to eliminate the deviation of multiple environments to optimize vehicle positioning accuracy in multiple environments using one model. The simulation results show that, compared with the existing methods, the proposed system architecture not only improves the performance but also effectively reduces the amount of data required for training.

Millimeter wave (mmWave) is a key technology for fifth-generation (5G) and beyond communications. Hybrid beamforming has been proposed for large-scale antenna systems in mmWave communications. Existing hybrid beamforming designs based on infinite-resolution phase shifters (PSs) are impractical due to hardware cost and power consumption. In this paper, we propose an unsupervised-learning-based scheme to jointly design the analog precoder and combiner with low-resolution PSs for multiuser multiple-input multiple-output (MU-MIMO) systems. We transform the analog precoder and combiner design problem into a phase classification problem and propose a generic neural network architecture, termed the phase classification network (PCNet), capable of producing solutions of various PS resolutions. Simulation results demonstrate the superior sum-rate and complexity performance of the proposed scheme, as compared to state-of-the-art hybrid beamforming designs for the most commonly used low-resolution PS configurations.

Remote vital sign monitoring systems have attracted attention from researchers as a non-contact solution for the detection of health issues. This assumes significance during the epidemic outbreak, where infections can be transmitted through contact. This paper proposes a deep learning (DL)-aided weighted scheme, where observations are fused, for implementing a vital sign system for fast acquisition and accurate estimation by considering spatial correlations. The specially designed weighted technique properly fuses spatial features to reduce the required size of the observation signal, thereby accomplishing the goal of fast acquisition. Moreover, a convolutional neural network (CNN) is employed to extract hidden information for accurate vital sign detection by performing two-dimensional convolution operations, which effectively utilizes spatial diversity to improve detection performance. Experimental results show that the proposed data-fusion-based scheme achieves satisfactory performance with limited observations for fast acquisition. Furthermore, its performance is comparable to that of conventional contact equipment; the absolute error of 90% breathing measurements is less than 3 respirations per minute (rpm), and the absolute error of 75% heartbeat measurements is less than 3 beats per minute (bpm), thereby confirming the potential of the proposed scheme.

Designing hybrid beamforming transceivers in millimeter wave (mmWave) MIMO-OFDM systems with satisfactory performance and acceptable complexity is a challenging problem. The well-known weighted minimum mean square error manifold optimization (WMMSE-MO) algorithm offers desired performance but has high computational complexity. In this paper, we propose to apply the deep unfolding technique to the WMMSE-MO algorithm. The proposed deep unfolding model yields faster convergence to better solutions as compared to the original algorithm. Simulation results demonstrate remarkable spectral efficiency performance with reduced computational time complexity for the proposed scheme, under different hardware (RF chains) and algorithmic (inner/outer iterations) settings for a massive MIMO-OFDM system.

Recently, non-contact vital sign estimating devices, which are used for health monitoring, have gradually gained interest among researchers. However, most of these devices have the disadvantages of high power consumption and high cost, which limit their practicality. Therefore, a less-expensive radar-based system is suggested for long-term health monitoring. Existing radar-based vital sign estimating schemes introduce unacceptable estimating errors. In order to improve the precision and stability, we employ Newtonized Orthogonal Matching Pursuit (NOMP) algorithm. NOMP provides better estimating results compared to existing schemes in vital sign estimation tasks. However, the performance of NOMP deteriorates severely under conditions of low signal-to-noise ratio, which causes poor power efficiency. In this study, we propose deep learning (DL)-aided NOMP schemes to tackle the aforementioned issue. Our simulation results and over the air measurements suggest that DL-aided NOMP schemes are superior to existing schemes.